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feat(eval): add missing minimax backend configuration

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Add missing configuration setup in scripts/eval_only.py to properly
support the minimax_chat backend, which was entirely omitted.

Fix the following coverage gaps in eval_only.py:
- Add minimax CLI arguments
- Include the minimax config mappings in _MAP
- Update the backend parsing logic
- Call configure_minimax_chat
This commit is contained in:
Yifan Yang
2026-06-23 20:31:39 +08:00
committed by Tanmay9223
parent 9969a8f393
commit 6849e609a3
1 changed files with 36 additions and 1 deletions
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37
scripts/eval_only.py
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@@ -29,6 +29,7 @@ from skillopt.model import (
configure_claude_code_exec,
configure_codex_exec,
configure_qwen_chat,
configure_minimax_chat,
set_reasoning_effort,
set_target_backend,
set_target_deployment,
@@ -138,7 +139,7 @@ def parse_args() -> argparse.Namespace:
# Legacy flat overrides
p.add_argument("--env", type=str)
p.add_argument("--backend", type=str,
choices=["azure_openai", "codex", "codex_exec", "claude", "claude_chat", "claude_code_exec"])
choices=["azure_openai", "codex", "codex_exec", "claude", "claude_chat", "claude_code_exec", "minimax", "minimax_chat"])
p.add_argument("--optimizer_model", type=str)
p.add_argument("--target_model", type=str)
p.add_argument("--optimizer_backend", type=str)
@@ -180,6 +181,12 @@ def parse_args() -> argparse.Namespace:
p.add_argument("--claude_code_exec_use_sdk", type=str)
p.add_argument("--claude_code_exec_effort", type=str)
p.add_argument("--claude_code_exec_max_thinking_tokens", type=int)
p.add_argument("--minimax_base_url", type=str)
p.add_argument("--minimax_api_key", type=str)
p.add_argument("--minimax_model", type=str)
p.add_argument("--minimax_temperature", type=float)
p.add_argument("--minimax_max_tokens", type=int)
p.add_argument("--minimax_enable_thinking", type=_BOOL)
p.add_argument("--out_root", type=str)
p.add_argument("--data_path", type=str)
p.add_argument("--split_mode", type=str,
@@ -255,6 +262,12 @@ def main() -> None:
"claude_code_exec_use_sdk": "model.claude_code_exec_use_sdk",
"claude_code_exec_effort": "model.claude_code_exec_effort",
"claude_code_exec_max_thinking_tokens": "model.claude_code_exec_max_thinking_tokens",
"minimax_base_url": "model.minimax_base_url",
"minimax_api_key": "model.minimax_api_key",
"minimax_model": "model.minimax_model",
"minimax_temperature": "model.minimax_temperature",
"minimax_max_tokens": "model.minimax_max_tokens",
"minimax_enable_thinking": "model.minimax_enable_thinking",
"seed": "train.seed",
"test_env_num": "evaluation.test_env_num",
"env": "env.name",
@@ -312,6 +325,9 @@ def main() -> None:
elif backend == "claude_code_exec":
cfg.setdefault("optimizer_backend", "openai_chat")
cfg.setdefault("target_backend", "claude_code_exec")
elif backend in {"minimax", "minimax_chat"}:
cfg.setdefault("optimizer_backend", "openai_chat")
cfg.setdefault("target_backend", "minimax_chat")
else:
cfg.setdefault("optimizer_backend", "openai_chat")
cfg.setdefault("target_backend", "openai_chat")
@@ -337,6 +353,15 @@ def main() -> None:
and not _has_model_override("model.target", "target_model")
):
cfg["target_model"] = default_model_for_backend("claude_chat")
if cfg.get("target_backend") == "minimax_chat":
if (
str(cfg.get("target_model", "") or "").strip() in _OPENAI_DEFAULT_MODEL_SENTINELS
and not _has_model_override("model.target", "target_model")
):
cfg["target_model"] = (
cfg.get("minimax_model")
or default_model_for_backend("minimax_chat")
)
if not cfg.get("out_root"):
env = cfg.get("env", "unknown")
@@ -416,6 +441,16 @@ def main() -> None:
target_max_tokens=cfg.get("target_qwen_chat_max_tokens"),
target_enable_thinking=cfg.get("target_qwen_chat_enable_thinking"),
)
configure_minimax_chat(
base_url=cfg.get("minimax_base_url") or None,
api_key=cfg.get("minimax_api_key") or None,
temperature=cfg.get("minimax_temperature"),
max_tokens=cfg.get("minimax_max_tokens"),
enable_thinking=cfg.get("minimax_enable_thinking"),
)
minimax_model_cfg = cfg.get("minimax_model")
if minimax_model_cfg and cfg.get("target_backend") == "minimax_chat":
set_target_deployment(str(minimax_model_cfg))
set_reasoning_effort(cfg.get("reasoning_effort", "") or None)
# Build adapter